Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
  • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
  • A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
  • A61M5/172—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic
  • A61M5/1723—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic using feedback of body parameters, e.g. blood-sugar, pressure
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
  • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
  • A61M5/142—Pressure infusion, e.g. using pumps
  • A61M5/14244—Pressure infusion, e.g. using pumps adapted to be carried by the patient, e.g. portable on the body
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
  • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M2205/00—General characteristics of the apparatus
  • A61M2205/35—Communication
  • A61M2205/3576—Communication with non implanted data transmission devices, e.g. using external transmitter or receiver

Definitions

• Diabetes mellitus is a chronic metabolic disorder caused by an inability of the pancreas to produce sufficient amounts of the hormone insulin, resulting in the decreased ability of the body to metabolize glucose.
• This failure leads to hyperglycemia, i.e. the presence of an excessive amount of glucose in the blood plasma.
• Persistent hyperglycemia and/or hypoinsulinemia has been associated with a variety of serious symptoms and life threatening long term complications such as dehydration, ketoacidosis, diabetic coma, cardiovascular diseases, chronic renal failure, retinal damage and nerve damages with the risk of amputation of extremities.
• a permanent therapy is necessary which provides constant glycemic control in order to always maintain the level of blood glucose within normal limits.
• Such glycemic control is achieved by regularly supplying external insulin to the body of the patient to thereby reduce the elevated levels of blood glucose.
• External biologic such as insulin was commonly administered by means of multiple daily injections of a mixture of rapid and intermediate acting drugs via a hypodermic syringe. It has been found that the degree of glycemic control achievable in this way is suboptimal because the delivery is unlike physiological hormone production, according to which hormone enters the bloodstream at a lower rate and over a more extended period of time.
• Improved glycemic control may be achieved by the so-called intensive hormone therapy which is based on multiple daily injections, including one or two injections per day of a long acting hormone for providing basal hormone and additional injections of a rapidly acting hormone before each meal in an amount proportional to the size of the meal.
• intensive hormone therapy is based on multiple daily injections, including one or two injections per day of a long acting hormone for providing basal hormone and additional injections of a rapidly acting hormone before each meal in an amount proportional to the size of the meal.
• Substantial improvements in diabetes therapy have been achieved by the development of the drug delivery device, relieving the patient of the need for syringes or drug pens and the administration of multiple daily injections.
• the drug delivery device allows for the delivery of drug in a manner that bears greater similarity to the naturally occurring physiological processes and can be controlled to follow standard or individually modified protocols to give the patient better glycemic control.
• Drug delivery devices can be constructed as an implantable device for subcutaneous arrangement or can be constructed as an external device with an infusion set for subcutaneous infusion to the patient via the transcutaneous insertion of a catheter, cannula or a transdermal drug transport such as through a patch.
• External drug delivery devices are mounted on clothing, hidden beneath or inside clothing, or mounted on the body and are generally controlled via a user interface built-in to the device or on a separate remote device.
• Blood or interstitial glucose monitoring is required to achieve acceptable glycemic control.
• delivery of suitable amounts of insulin by the drug delivery device requires that the patient frequently determines his or her blood glucose level and manually inputs this value into a user interface for the external pumps, which then calculates a suitable modification to the default or currently in-use insulin delivery protocol, i.e. dosage and timing, and subsequently communicates with the drug delivery device to adjust its operation accordingly.
• the determination of blood glucose concentration is typically performed by means of an episodic measuring device such as a hand-held electronic meter which receives blood samples via enzyme-based test strips and calculates the blood glucose value based on the enzymatic reaction.
• CGM Continuous glucose monitoring
• PID controllers have been utilized with mathematical model of the metabolic interactions between glucose and insulin in a person.
• the PID controllers can be tuned based on simple rules of the metabolic models. However, when the PID controllers are tuned or configured to aggressively regulate the blood glucose levels of a subject, overshooting of the set level can occur, which is often followed by oscillations, which is highly undesirable in the context of regulation of blood glucose.
• MPC model predictive controller
• MPC can be viewed therefore as a combination of feedback and feedforward control.
• MPC typically requires a metabolic model to mimic as closely as possible to the interaction between insulin and glucose in a biological system.
• MPC models continue to be further refined and developed and details of the MPC controllers, variations on the MPC and mathematical models representing the complex interaction of glucose and insulin are shown and described in the following documents:
• Pancreatic ⁇ -Cell Diabetes Technology and Therapeutics, Vol. 12, No. 11, 2010; and [0019] Percival et al.., "Closed-Loop Control of an Artificial Pancreatic ⁇ -Cell Using Multi- Parametric Model Predictive ControF Diabetes Research 2008.
• Atlas demonstrated in 2010 that a combination of CTR and CTT can be utilized to quite good efficacy in managing diabetes. Atlas, however, failed to describe or illustrate how the CTR and CTT were to be utilized in his experiments. Specifically, Atlas failed to show or describe the interplay between CTR and CTT and whether both CTR and CTT were utilized separately or concurrently.
• Applicants have recognized that a key requirement in the utilization of CTR and CTT is knowing when to switch from CTR to CTT and vice versa. Accordingly, applicants have devised a technique to allow a controller to utilize the appropriate technique for insulin dosing in a diabetes management system, such as, for example, an artificial pancreas.
• a method to control an infusion pump with a model-predictive-controller and receive data from at least one glucose sensor is provided.
• the method can be achieved by: measuring glucose level in the subject from the glucose sensor to provide at least one glucose measurement in each time interval; predicting at least one future glucose value based on the glucose measurements made in the measuring step; evaluating whether the at least one future glucose value is within a predetermined range of glucose values, in the event the at least one future glucose value is not within the range, determining an insulin amount with the model- predictive controller based on a target value otherwise determining an insulin amount with the model-predictive-controller based on the predetermined range; and delivering the insulin in the amount determined in the determining step.
• a system for management of diabetes that includes a continuous glucose sensor, an insulin infusion pump and a controller.
• the continuous glucose monitor is configured to continuously measure glucose level of the subject at discrete generally uniform time intervals and provide the glucose level at each interval in the form of glucose measurement data.
• the insulin infusion pump is configured to deliver insulin.
• the controller is in communication with the pump, glucose meter and the glucose monitor in which the controller is configured to predict at least one future glucose value based on prior glucose measurement data from the continuous glucose monitor, and evaluate whether the at least one future glucose value is within a predetermined range of glucose values and in the event the at least one future glucose value is not within the range, a determination is made of an insulin amount with the model-predictive controller based on a target value otherwise a determination is made of an insulin amount with the model-predictive-controller based on the predetermined range and command the insulin infusion pump to deliver the insulin amount determined by the controller.
• Figure 1 illustrates the system in which a controller for the pump or glucose monitor(s) is separate from both the infusion pump and the glucose monitor(s) and in which a network can be coupled to the controller to provide near real-time monitoring.
• Figure 2A illustrates an exemplary embodiment of the diabetic management system in schematic form.
• Figures 2B and 2C illustrate respective examples of conceptual costs in MPC calculations (the sum of the red areas) for an artificial pancreas application for a) control-to- target and b) control-to-range techniques.
• Figure 3 illustrates the logic utilized in the controller of Figure 1 or Figure 2A.
• Figures 4A and 4B illustrate example A for a CTR-MPC mode.
• Figures 5 A and 5B illustrate example B for a switchover from a CTR-MPC mode to a CTT-MPC mode.
• the terms “about” or “approximately” for any numerical values or ranges indicate a suitable dimensional tolerance that allows the part or collection of components to function for its intended purpose as described herein.
• the terms “patient,” “host,” “user,” and “subject” refer to any human or animal subject and are not intended to limit the systems or methods to human use, although use of the subject invention in a human patient represents a preferred embodiment.
• the term “user” includes not only the patient using a drug infusion device but also the caretakers (e.g., parent or guardian, nursing staff or home care employee).
• the term “drug” may include hormone, biologically active materials, pharmaceuticals or other chemicals that cause a biological response (e.g., glycemic response) in the body of a user or patient.
• Figure 1 illustrates a drug delivery system 100 according to an exemplary embodiment that utilizes the principles of the invention.
• Drug delivery system 100 includes a drug delivery device 102 and a remote controller 104.
• Drug delivery device 102 is connected to an infusion set 106 via flexible tubing 108.
• Drug delivery device 102 is configured to transmit and receive data to and from remote controller 104 by, for example, radio frequency communication 1 12.
• Drug delivery device 102 may also function as a stand-alone device with its own built in controller.
• drug delivery device 102 is an insulin infusion device and remote controller 104 is a hand-held portable controller.
• data transmitted from drug delivery device 102 to remote controller 104 may include information such as, for example, insulin delivery data, blood glucose information, basal, bolus, insulin to carbohydrates ratio or insulin sensitivity factor, to name a few.
• the controller 104 is configured to include an MPC controller 10 that has been programmed to receive continuous glucose readings from a CGM sensor 112.
• Data transmitted from remote controller 104 to insulin delivery device 102 may include glucose test results and a food database to allow the drug delivery device 102 to calculate the amount of insulin to be delivered by drug delivery device 102.
• the remote controller 104 may perform basal dosing or bolus calculation and send the results of such calculations to the drug delivery device.
• an episodic blood glucose meter 1 14 may be used alone or in conjunction with the CGM sensor 1 12 to provide data to either or both of the controller 104 and drug delivery device 102.
• the remote controller 104 may be combined with the meter 1 14 into either (a) an integrated monolithic device; or (b) two separable devices that are dockable with each other to form an integrated device.
• Each of the devices 102, 104, and 1 14 has a suitable micro-controller (not shown for brevity) programmed to carry out various functionalities.
• Drug delivery device 102 may also be configured for bi-directional wireless communication with a remote health monitoring station 1 16 through, for example, a wireless communication network 1 18.
• Remote controller 104 and remote monitoring station 116 may be configured for bi-directional wired communication through, for example, a telephone land based communication network.
• Remote monitoring station 1 16 may be used, for example, to download upgraded software to drug delivery device 102 and to process information from drug delivery device 102.
• Examples of remote monitoring station 116 may include, but are not limited to, a personal or networked computer 126, server 128 to a memory storage, a personal digital assistant, other mobile telephone, a hospital base monitoring station or a dedicated remote clinical monitoring station.
• Drug delivery device 102 includes processing electronics: including a central processing unit and memory elements for storing control programs and operation data, a radio frequency module 1 16 for sending and receiving communication signals (i.e., messages) to/from remote controller 104, a display for providing operational information to the user, a plurality of navigational buttons for the user to input information, a battery for providing power to the system, an alarm (e.g., visual, auditory or tactile) for providing feedback to the user, a vibrator for providing feedback to the user, a drug delivery mechanism (e.g. a drug pump and drive mechanism) for forcing a insulin from a insulin reservoir (e.g., a insulin cartridge) through a side port connected to an infusion set 108/106 and into the body of the user.
• a drug delivery mechanism e.g. a drug pump and drive mechanism for forcing a insulin from a insulin reservoir (e.g., a insulin cartridge) through a side port connected to an infusion set 108/106 and into the body of the user.
• Glucose levels or concentrations can be determined by the use of the CGM sensor 1 12.
• the CGM sensor 112 utilizes amperometric electrochemical sensor technology to measure glucose with three electrodes operably connected to the sensor electronics and are covered by a sensing membrane and a biointerface membrane, which are attached by a clip.
• the top ends of the electrodes are in contact with an electrolyte phase (not shown), which is a free-flowing fluid phase disposed between the sensing membrane and the electrodes.
• the sensing membrane may include an enzyme, e.g., glucose oxidase, which covers the electrolyte phase.
• the counter electrode is provided to balance the current generated by the species being measured at the working electrode.
• the species being measured at the working electrode is H2O2.
• the current that is produced at the working electrode (and flows through the circuitry to the counter electrode) is proportional to the diffusional flux of H2O2.
• a raw signal may be produced that is representative of the concentration of glucose in the user's body, and therefore may be utilized to estimate a meaningful glucose value. Details of the sensor and associated components are shown and described in US Patent No. 7,276,029, which is incorporated by reference herein as if fully set forth herein this application. In one embodiment, a continuous glucose sensor from the Dexcom Seven System (manufactured by Dexcom Inc.) can also be utilized with the exemplary embodiments described herein.
• the following components can be utilized as a system for management of diabetes that is akin to an artificial pancreas: OneTouch Ping® Glucose Management System by Animas Corporation that includes at least an infusion pump and an episodic glucose sensor; and DexCom® SEVEN PLUS® CGM by DexCom Corporation with interface to connect these components and programmed in MATLAB®language and accessory hardware to connect the components together; and control algorithms in the form of an MPC that automatically regulates the rate of insulin delivery based on the glucose level of the patient, historical glucose measurement and anticipated future glucose trends, and patient specific information.
• OneTouch Ping® Glucose Management System by Animas Corporation that includes at least an infusion pump and an episodic glucose sensor
• DexCom® SEVEN PLUS® CGM by DexCom Corporation with interface to connect these components and programmed in MATLAB®language and accessory hardware to connect the components together
• control algorithms in the form of an MPC that automatically regulates the rate of insulin delivery based on the glucose level of the patient, historical glucose measurement
• Figure 2A illustrates a schematic diagram 200 of the system 100 in Figure 1 programmed with the solution devised by applicants to counteract a less than desirable effect of a closed-loop control system.
• Figure 2A provides for an MPC programmed into a control logic module 10 that is utilized in controller 104.
• MPC enabled module 10 receives a desired glucose concentration or range of glucose concentration 12 (along with any modification from an update filter 28 so that it is able to maintain the output (i.e., glucose level) of the subject within the desired range of glucose levels.
• the first output 14 of the MPC-enabled control logic 10 can be a control signal to an insulin pump 16 to deliver a desired quantity of insulin 18 into a subject 20 at predetermined time intervals, which can be indexed every 5 minutes using time interval index k.
• a second output in the form of a predicted glucose value 15 can be utilized in control junction B.
• a glucose sensor 22 (or 112 in Fig. 1) measures the glucose levels in the subject 20 in order to provide signals 24 representative of the actual or measured glucose levels to control junction B, which takes the difference between measured glucose concentration 24 and the MPC predictions of that measured glucose concentration. This difference provides input for the update filter 26 of state variables of the model.
• the difference 26 is provided to an estimator (also known as an update filter 28) that provides for estimate of state variables of the model that cannot be measured directly.
• the update filter 28 is preferably a recursive filter in the form of a Kalman filter with tuning parameters for the model.
• the output of the update or recursive filter 28 is provided to control junction A whose output is utilized by the MPC in the control logic 10 to further refine the control signal 14 to the pump 16 (or 102 in Fig. 1).
• control-to-target or CTT
• control-to-range or CTR.
• the controller attempts to drive the controlled variable(s) to desired levels by adjusting the manipulated variable(s).
• CTT the controller attempts to drive the controlled variable(s) to a specific target value, a.k.a. set point; in a CTR scheme, on the other hand, the controller attempts to keep the controlled variable(s) within a target range of values.
• CTT approaches are useful for systems whose controlled variables must be maintained as close to a certain value as possible; CTR approaches, on the other hand, are useful for systems whose controlled variable can fluctuate safely between a lower and an upper limit. In the latter case, the fewer the control moves (i.e., adjustments of the manipulated variable away from its steady-state set point or target range), the better.
• the solution to the control problem is that value (or those values) of the manipulated variable(s) that result in the smallest predicted conceptual "cost" for the future.
• An example of a "cost” is the predicted deviation of the controlled variable away from the desired level. More specifically, it could be the absolute integral of the predicted trajectory of the controlled variable away from the set point (in the case of CTT), or outside of the acceptable range (in the case of CTR).
• An illustration of these two cases for an artificial pancreas application is shown in Figs. 2B and 2C.
• the glucose prediction from the CTT-MPC model is 202
• the dashed line 204 is the target value
• the cost is the sum of the shaded areas 204 and 206.
• the dashed lines 204a and 204b represent the range or zone in which a prediction of future glucose 202' must be within this zone.
• the cost in the CTR-MPC model in Fig. 2C is the shaded areas 204' and 206'.
• a second conceptual cost might be the analogous deviation of the manipulated variable(s) away from its steady-state set point or target range. Assigning such a cost to this deviation is a way of preventing absolute reliance on the measurements of the controlled variable, an important safeguard in applications for which there is known inaccuracy in the sensor (e.g., CGM noise, drift, and/or RF
• the MPC logic is formulated to control a subject glucose level to a safe glucose zone, with the lower blood glucose limit of the zone varying between 80-100 mg/dL and the upper blood glucose limit varying between about 140-180 mg/dL; the algorithm will henceforth be referred to as the "Zone MPC".
• Controlling to a target zone is, in general, applied to controlled systems that lack a specific set point with the controller's goal being to keep the controlled variable (CV) in a predefined zone.
• Control to zone i.e., a normaglycemic zone
• an inherent benefit of control to zone is the ability to limit pump
• the insulin delivery rate ID from the Zone MPC law is calculated by an online optimization, which evaluates at each sampling time the next insulin delivery rate.
• the optimization at each sampling time is based on the estimated metabolic state (plasma glucose, subcutaneous insulin) obtained from the dynamic model stored in module 10.
• the MPC of control logic 10 incorporates an explicit model of human T1DM glucose- insulin dynamics.
• the model is used to predict future glucose values and to calculate future controller moves that will bring the glucose profile to the desired range or "Zone.”
• G k) a ⁇ G k - 1) + a 2 G k - 2) + a 3 G'(£ - 3) + a 4 G'(k - 4) + a 5 G k - 5) + bI M (k - 4)
• G' is the measured glucose concentration is the "mapped insulin" which is not a measured quantity is the delivered insulin or a manipulated variable and coefficients a x ⁇ 2.993; a 2 ⁇ (-3.775); a 3 ⁇ 2.568; a 4 ⁇ (-0.886); a 5 ⁇ 0.09776; b ⁇ (-1.5); ci-1.665; c 2 ⁇ (-0.693); di ⁇ 0.01476; d 2 ⁇ 0.01306.
• Equation (2) Using the FDA accepted metabolic simulator known to those skilled in the art, Eq. (1) can be reduced to the following linear difference model in Equation (2):
• Meal M (k) 1.50 ⁇ Meal M (k-Y) + 0.5427 Meal M (k - 2)
• G' is the glucose concentration output (G) deviation variable (mg/dL),
• ID' is the insulin infusion rate input (I D ) deviation variable (U/h), i.e.,
• Meal is the CHO ingestion input (gram- CHO)
• IM is the mapped subcutaneous insulin infusion rates (U/h)
• Meal M is the mapped CHO ingestion input (gram- CHO).
• the dynamic model in Eq. (2) relates the effects of insulin infusion rate (ID), and CHO ingestion input (Meal) on plasma glucose.
• ID insulin infusion rate
• Meal CHO ingestion input
• the model represents a single average model for the total population of subjects.
• the model and its parameters are fixed.
• the second-order input transfer functions described by parts (b) and (c) in Eq. (2) are used to generate an artificial input memory in the Zone MPC schema to prevent insulin overdosing, and consequently prevent hypoglycemia.
• the evaluation of any sequential insulin delivery must take into consideration the past administered insulin against the length of the insulin action.
• a one-state linear difference model with a relatively low order uses the output (glycemia) as the main source of past administered input (insulin) "memory.”
• the output glycemia
• insulin insulin
• this may result in under- or over-delivery of insulin.
• IM and Mealm two additional states for the mapped insulin and meal inputs that carry a longer insulin memory.
• Zone MPC The CTR technique in the context of zone MPC (“Zone MPC") is applied when the specific set point value of a controlled variable ("CV") (in the form of glucose value) is of low relevance compared to a zone that is defined by upper and lower boundaries or a range of the CV. Moreover, in the presence of noise and model mismatch there is no practical value using a fixed set point.
• Zone MPC was developed through research by the University of California at Santa Barbara and the Sansuro Diabetes R esearch Institute. Other details of the derivation for the Zone MPC technique are shown and described in Benyamin Grosman, Ph.D., Eyal Dassau, Ph.D., Howard C. Zisser, M.D., Lois Jovanovic, M.D., and Francis J.
• Zone MPC Zone MPC
• Zone MPC is implemented by defining fixed upper and lower bounds as soft constraints by letting the optimization weights switch between zero and some final values when the predicted CVs are in or out of the desired zone, respectively.
• the predicted residuals are generally defined as the difference between the CV that is out of the desired zone and the nearest bound.
• Zone MPC is typically divided into three different zones. The permitted range is the control target and it is defined by upper and lower bounds.
• the upper zone represents undesirable high predicted glycemic values.
• the lower zone represents undesirable low predicted glycemic values that represent hypoglycemic zone or a pre-hypoglycemic protective area that is a low alarm zone.
• the Zone MPC optimizes the predicted glycemia by manipulating the near- future insulin control moves to stay in the permitted zone under specified constrains.
• Zone MPC lies in its cost function formulation that holds the zone formulation.
• Zone MPC like any other forms of MPC, predicts the future output by an explicit model using past input/output records and future input moves that need to be optimized. However, instead of driving to a specific fixed set point, the optimization attempts to keep or move the predicted outputs into a zone that is defined by upper and lower bounds. Using a linear difference model, the glycemic dynamics are predicted and the optimization reduces future glycemic excursions from the zone under constraints and weights defined in its cost function.
• Zone MPC cost function J used in the presented work is defined as follows:
• J ⁇ I D ') ⁇ (k + j) ⁇ + R - ⁇ ⁇ I D (k + j) - basal ⁇ k + (4)
• Q is a weighting factor on the predicted glucose term
• R is a tuning factor on the future proposed inputs in the cost function
• vector I D contains the set of proposed near-future insulin infusion amounts. It is the "manipulated variable” because it is manipulated in order to find the minimum in J.
• G ZONE is a variable quantifying the deviation of future model-predicted CGM values G outside a specified glycemic zone, and is determined by making the following comparisons: where the glycemic zone is defined by the upper limit G ZH and the lower limit G ZL -
• Equation (2)-(5) In order to solve optimization problem of Equations (2)-(5), a commercially available software (e.g., MATLAB's "fmincon.m” function) is utilized. For this function, the following parameters are used for each optimization:
• Max _f 100* , where M is control horizon as described earlier.
• Max_i 400, which is fixed.
• Termination tolerance Term ol on the manipulated variables I D is le-6.
• basal ⁇ I ' ⁇ 72 U/h (6) where basal is the subject's basal rate as set by the subject or his/her physician, expected in the range 0.6 - 1.8 U/hr.
• control horizontal parameter M and prediction horizon parameter P have significant effects on the controller performance, and are normally used to tune an MPC based controller, they can be heuristically tuned based on knowledge of the system. Tuning rules are known to those skilled in the field. According to these rules M and P may vary between:
• the ratio of the output error weighting factor Q and the input change weighting matrix or tuning factor R may vary between:
• Control-To-Target MPC Mode [0071]
• the control to target technique in the context of MPC is a simplification of the control to range method in which, effectively, the "range" is one CGM value, e.g., 1 10 mg/dL, and therefore has zero width.
• the "range" is one CGM value, e.g., 1 10 mg/dL, and therefore has zero width.
• One realization of a control to target control law, based on the equations given above for the control-to-range technique, would be achieved by rewriting Eq. 5 as
• the structure of the penalty for a glucose prediction might take different forms. As shown in Eq. 9, the penalty of a given predicted glucose value is equivalent to its absolute deviation from G sp . However, it is straightforward to impose different penalty structures such that the penalty of a given predicted glucose value is equivalent to the square of its deviation from G Sp , for example.
• the technique 300 begins with the appropriate sensor (e.g., CGM sensor) measuring a glucose level in the subject to provide at least one glucose measurement in each time interval in a series of discrete time interval index ("k") at step 302.
• the MPC controller 10 is utilized in a predicting of at least one future glucose value based on the glucose measurements made in the measuring step.
• the system evaluates as to whether the at least one future glucose value is within a predetermined range of glucose values.
• a determination of an insulin amount with the model-predictive controller is made based on a target value, as described above. Otherwise, if the evaluation step 306 returns a yes, the logic moves to step 310 in which a determination is made of an insulin amount with the model- predictive-controller based on the predetermined range. At step 312, the system delivers the insulin in the amount determined in the determining step (i.e., step 308 or step 310).
• a system is provided that includes a continuous glucose monitor 22, an insulin infusion pumpl6, and a controller 10.
• the monitor 22 is configured to continuously measure glucose level of the subject 20 at discrete time intervals and provide the glucose level at each interval in the form of glucose measurement data.
• the pump is configured to deliver insulin to a subject.
• the controller is in communication with the pump, glucose meter and the glucose monitor.
• the controller is configured to: (a) predict at least one future glucose value based on prior glucose measurement data from the continuous glucose monitor 22, (b) evaluate whether the at least one future glucose value is within a predetermined range of glucose values and in the event the at least one future glucose value is not within the range, a determination is made of an insulin amount with the model-predictive controller based on a target value otherwise a determination is made of an insulin amount with the model-predictive-controller based on the predetermined range and (c) command the insulin infusion pump 16 to deliver the insulin amount determined by the controller.
• the glucose target range is 90-140 mg/dL, and the subject's basal rate is 1 U/h.
• the target glucose value (CTT set point) is 140 mg/dL.
• Table A The most recent five CGM values from oldest to newest is plotted at 402 in Fig. 4A.
• the system has been delivering a basal dose of 1 U/h; which maps to a 1/12 U injection every 5 minutes in the recent history at 404.
• the MPC controller 10 determines future or predicted blood glucose values 406 and shows that (assuming future basal delivery 408) there would likely be no breach by actual blood glucose value based on the predicted future blood glucose values 406.
• the basal insulin in this case 1 U/h
• the target zone can be generally constant. However, in certain configurations, the target zone can vary as shown here in Fig. 4B.
• a switch from a CTR-MPC to CTT-MPC can be made under certain circumstances, as shown in Figures 5A and 5B in this example B.
• Table B the most recent CGM values from oldest to newest are provided.
• Table B The most recent five CGM values from oldest to newest are plotted at 502 in Fig. 5A.
• the controller 10 in example B has been commanding the pump to deliver a basal rate of 1 Units per hour (U/h) (or 1/12 Unit every 5 minute at 504 in Fig. 5A.
• U/h Units per hour
• a prediction into the future by the MPC controller shows that (assuming the same basal insulin rate as before at 505), there would likely be a clear excursion of the blood glucose values 506 in the subject above the target zone 508.
• the evaluating step would return a "no" and the logic would switch over from a CTR mode to a CTT mode for the insulin dosing by the pump.
• the MPC controller will determine, in the CTT mode of Figure 5B, the appropriate insulin infusion in the near future (e.g., the next five insulin infusion amounts) at 510 (with a sharp spike to 0.76 Unit at 511 followed by the previous basal amount of 1/12 Unit) such that the predicted future glucose values 512 would trend sharply downward so as to be under the CTT set point of 140 mg/dL.
• the appropriate insulin infusion in the near future e.g., the next five insulin infusion amounts
• the predicted future glucose values 512 would trend sharply downward so as to be under the CTT set point of 140 mg/dL.
• the closed-loop controller need not be an MPC controller but can be, with appropriate modifications by those skilled in the art, a PID controller, a PID controller with internal model control (IMC), a model-algorithmic-control (MAC) that are discussed by Percival et al., in "Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic ⁇ Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers" Journal of Diabetes Science and Technology, Vol. 2, Issue 4, July 2008.
• IMC internal model control
• MAC model-algorithmic-control

Landscapes

• Health & Medical Sciences (AREA)
• Engineering & Computer Science (AREA)
• Public Health (AREA)
• General Health & Medical Sciences (AREA)
• Biomedical Technology (AREA)
• Medical Informatics (AREA)
• Veterinary Medicine (AREA)
• Life Sciences & Earth Sciences (AREA)
• Animal Behavior & Ethology (AREA)
• Heart & Thoracic Surgery (AREA)
• Anesthesiology (AREA)
• Vascular Medicine (AREA)
• Hematology (AREA)
• Epidemiology (AREA)
• Primary Health Care (AREA)
• Diabetes (AREA)
• Medicinal Chemistry (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Chemical & Material Sciences (AREA)
• Data Mining & Analysis (AREA)
• Databases & Information Systems (AREA)
• Pathology (AREA)
• External Artificial Organs (AREA)
• Infusion, Injection, And Reservoir Apparatuses (AREA)

Priority Applications (9)

Applications Claiming Priority (2)

Publications (2)

Family

ID=49943524

Family Applications (1)

Country Status (12)

Families Citing this family (69)

Citations (6)

Family Cites Families (29)

• 2012
  • 2012-12-20 US US13/722,329 patent/US9907909B2/en not_active Expired - Fee Related
• 2013
  • 2013-12-17 BR BR112015014941A patent/BR112015014941A2/pt not_active Application Discontinuation
  • 2013-12-17 RU RU2015129489A patent/RU2015129489A/ru not_active Application Discontinuation
  • 2013-12-17 KR KR1020157019403A patent/KR20150097731A/ko not_active Application Discontinuation
  • 2013-12-17 CA CA2895501A patent/CA2895501A1/en not_active Abandoned
  • 2013-12-17 WO PCT/US2013/075617 patent/WO2014099882A2/en active Application Filing
  • 2013-12-17 EP EP13818889.1A patent/EP2936359A2/en not_active Withdrawn
  • 2013-12-17 CN CN201380067221.4A patent/CN104885086A/zh active Pending
  • 2013-12-17 AU AU2013363049A patent/AU2013363049A1/en not_active Abandoned
  • 2013-12-17 JP JP2015549555A patent/JP2016508042A/ja active Pending
  • 2013-12-18 TW TW102146817A patent/TW201503921A/zh unknown
• 2016
  • 2016-03-15 HK HK16102962.2A patent/HK1215086A1/zh unknown
• 2018
  • 2018-01-29 US US15/882,195 patent/US20180147349A1/en not_active Abandoned

Patent Citations (6)

Non-Patent Citations (17)

Also Published As

Similar Documents

Legal Events